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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments). Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7271-SERVIL-002/Default.aspx Requirements Research
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function based on a coupled NEMS network, consisting of 2 or more double-drum resonators. This is beyond current state of art and relies on deep understand of more degrees of nonlinear complexity
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and
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. Applications must include a CV, a cover letter, and transcripts from Master's 1 and 2. Title : Deep-learning for nuclear data in physics for health This PhD project aims to improve the modeling of nuclear
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, computational physics, or a related field; • Strong background in machine learning, probability, and statistics; • Proficiency in scientific programming with Python and deep-learning frameworks such as PyTorch
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. • Karimi H. et al., “Wavelet Based Protection of Microgrids”, IEEE Transactions on Smart Grid, 2019. • Heidari A. et al., “Deep Learning for Fault Detection in Smart Grids”, Applied Energy, 2021. • Wen L. et